Manipulacion_datos_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.468508Z",
     "start_time": "2019-11-01T15:44:12.562054Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creamos un Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.478192Z",
     "start_time": "2019-11-01T15:44:14.471473Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data={\"Pais\":['Mexico','Argentina','Espana','Colombia'],\n",
    "                        \"Poblacion\":[127212000, 45167000, 47099000, 48922000]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.504353Z",
     "start_time": "2019-11-01T15:44:14.481401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion\n",
       "0     Mexico  127212000\n",
       "1  Argentina   45167000\n",
       "2     Espana   47099000\n",
       "3   Colombia   48922000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ordenamos por columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.527018Z",
     "start_time": "2019-11-01T15:44:14.514223Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion\n",
       "0     Mexico  127212000\n",
       "3   Colombia   48922000\n",
       "2     Espana   47099000\n",
       "1  Argentina   45167000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values([\"Poblacion\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.539098Z",
     "start_time": "2019-11-01T15:44:14.529889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion\n",
       "1  Argentina   45167000\n",
       "3   Colombia   48922000\n",
       "2     Espana   47099000\n",
       "0     Mexico  127212000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values([\"Pais\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agregar una columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.550702Z",
     "start_time": "2019-11-01T15:44:14.541083Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie\n",
       "1  Argentina   45167000     1964375\n",
       "3   Colombia   48922000     2780400\n",
       "2     Espana   47099000      505944\n",
       "0     Mexico  127212000     1142748"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Superficie\"] = [1964375, 2780400, 505944, 1142748]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Asigno mismo valor a todas las filas en una columna nueva"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.562537Z",
     "start_time": "2019-11-01T15:44:14.552807Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Deporte</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "      <td>Futbol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "      <td>Futbol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "      <td>Futbol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "      <td>Futbol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie Deporte\n",
       "1  Argentina   45167000     1964375  Futbol\n",
       "3   Colombia   48922000     2780400  Futbol\n",
       "2     Espana   47099000      505944  Futbol\n",
       "0     Mexico  127212000     1142748  Futbol"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Deporte']= 'Futbol'\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eliminar una columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.578164Z",
     "start_time": "2019-11-01T15:44:14.566159Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie\n",
       "1  Argentina   45167000     1964375\n",
       "3   Colombia   48922000     2780400\n",
       "2     Espana   47099000      505944\n",
       "0     Mexico  127212000     1142748"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Deporte'],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eliminar multiples columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.595086Z",
     "start_time": "2019-11-01T15:44:14.581744Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Poblacion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>45167000</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>48922000</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>47099000</td>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>127212000</td>\n",
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      ],
      "text/plain": [
       "   Poblacion\n",
       "1   45167000\n",
       "3   48922000\n",
       "2   47099000\n",
       "0  127212000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Superficie','Pais'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.612566Z",
     "start_time": "2019-11-01T15:44:14.598332Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie\n",
       "1  Argentina   45167000     1964375\n",
       "3   Colombia   48922000     2780400\n",
       "2     Espana   47099000      505944\n",
       "0     Mexico  127212000     1142748"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # recuerda! como no lo asignamos, el df anterior sigue \"intacto\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agregar una fila nueva al final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.621927Z",
     "start_time": "2019-11-01T15:44:14.615114Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cantidad_filas = len(df) # obtengo la cantidad de filas\n",
    "cantidad_filas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.638448Z",
     "start_time": "2019-11-01T15:44:14.624353Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Benezuela</td>\n",
       "      <td>0</td>\n",
       "      <td>916445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie\n",
       "1  Argentina   45167000     1964375\n",
       "3   Colombia   48922000     2780400\n",
       "2     Espana   47099000      505944\n",
       "0     Mexico  127212000     1142748\n",
       "4  Benezuela          0      916445"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[cantidad_filas] = [\"Benezuela\", 0, 916445] # Está mal escrito el pais! (lo sé)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Actualizo la fila entera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.654066Z",
     "start_time": "2019-11-01T15:44:14.640609Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>0</td>\n",
       "      <td>916445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie\n",
       "1  Argentina   45167000     1964375\n",
       "3   Colombia   48922000     2780400\n",
       "2     Espana   47099000      505944\n",
       "0     Mexico  127212000     1142748\n",
       "4  Venezuela          0      916445"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[4] = [\"Venezuela\", 0, 916445]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Actualizo una celda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.669129Z",
     "start_time": "2019-11-01T15:44:14.656468Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>916445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie\n",
       "1  Argentina   45167000     1964375\n",
       "3   Colombia   48922000     2780400\n",
       "2     Espana   47099000      505944\n",
       "0     Mexico  127212000     1142748\n",
       "4  Venezuela   32423000      916445"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[4,'Poblacion'] = 32423000\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eliminar una fila"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.687725Z",
     "start_time": "2019-11-01T15:44:14.673498Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>916445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie\n",
       "1  Argentina   45167000     1964375\n",
       "2     Espana   47099000      505944\n",
       "0     Mexico  127212000     1142748\n",
       "4  Venezuela   32423000      916445"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eliminar multiples filas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.704514Z",
     "start_time": "2019-11-01T15:44:14.690302Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>916445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie\n",
       "2     Espana   47099000      505944\n",
       "0     Mexico  127212000     1142748\n",
       "4  Venezuela   32423000      916445"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([3,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtrar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paises con mas de 46 millones de habitantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.718480Z",
     "start_time": "2019-11-01T15:44:14.706795Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pais  Poblacion  Superficie\n",
       "3  Colombia   48922000     2780400\n",
       "2    Espana   47099000      505944\n",
       "0    Mexico  127212000     1142748"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mas_de_46 = df[ df['Poblacion'] > 46000000 ]\n",
    "mas_de_46"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Más de 46 mill y superficie menor a 600.000 Km2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.733229Z",
     "start_time": "2019-11-01T15:44:14.721385Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pais  Poblacion  Superficie\n",
       "2  Espana   47099000      505944"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doble_filtro = df[ (df['Poblacion'] > 46000000) & (df['Superficie'] < 600000) ]\n",
    "doble_filtro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Busco por un valor específico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.750037Z",
     "start_time": "2019-11-01T15:44:14.735910Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pais  Poblacion  Superficie\n",
       "3  Colombia   48922000     2780400"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "por_nombre = df[ df['Pais'] == 'Colombia' ]\n",
    "por_nombre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paises con nombre mayor a 6 letras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.768810Z",
     "start_time": "2019-11-01T15:44:14.752716Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>916445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie\n",
       "1  Argentina   45167000     1964375\n",
       "3   Colombia   48922000     2780400\n",
       "4  Venezuela   32423000      916445"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nombre_largo = df[ df['Pais'].str.len() > 6 ]\n",
    "nombre_largo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtrar por True/False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.783588Z",
     "start_time": "2019-11-01T15:44:14.771849Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>916445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie\n",
       "1  Argentina   45167000     1964375\n",
       "4  Venezuela   32423000      916445"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arreglo = [True,False,False,False,True] # Esto es lo que realmente hacemos en los casos anteriores!\n",
    "df[arreglo]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtener el indice de una fila"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T16:06:11.395330Z",
     "start_time": "2019-11-01T16:06:11.388462Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "por_nombre = df[ df['Pais'] == 'Colombia' ]\n",
    "por_nombre.index.tolist()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aplicar operaciones entre columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.802908Z",
     "start_time": "2019-11-01T15:44:14.786471Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Habit_x_km2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>916445</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie  Habit_x_km2\n",
       "3   Colombia   48922000     2780400           17\n",
       "1  Argentina   45167000     1964375           22\n",
       "4  Venezuela   32423000      916445           35\n",
       "2     Espana   47099000      505944           93\n",
       "0     Mexico  127212000     1142748          111"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# agregamos en una nueva columna el ratio de habitantes por superficie\n",
    "df['Habit_x_km2'] = (df['Poblacion'] / df['Superficie']).astype(int)\n",
    "df.sort_values(['Habit_x_km2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aplicar una operacion definida"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.813306Z",
     "start_time": "2019-11-01T15:44:14.808267Z"
    }
   },
   "outputs": [],
   "source": [
    "def crear_codigo(name):\n",
    "    name = name.upper() # paso a mayusculas\n",
    "    name = name[0:4]    # primeros 4 char\n",
    "    return name\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### aplicamos usando 1 columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.836817Z",
     "start_time": "2019-11-01T15:44:14.819827Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Habit_x_km2</th>\n",
       "      <th>Codigo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "      <td>22</td>\n",
       "      <td>ARGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "      <td>17</td>\n",
       "      <td>COLO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "      <td>93</td>\n",
       "      <td>ESPA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "      <td>111</td>\n",
       "      <td>MEXI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>916445</td>\n",
       "      <td>35</td>\n",
       "      <td>VENE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie  Habit_x_km2 Codigo\n",
       "1  Argentina   45167000     1964375           22   ARGE\n",
       "3   Colombia   48922000     2780400           17   COLO\n",
       "2     Espana   47099000      505944           93   ESPA\n",
       "0     Mexico  127212000     1142748          111   MEXI\n",
       "4  Venezuela   32423000      916445           35   VENE"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Codigo'] = df['Pais'].apply(crear_codigo)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aplicamos una función enviando cada fila (todas las columnas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.865479Z",
     "start_time": "2019-11-01T15:44:14.839881Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
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       "      <th>Codigo</th>\n",
       "      <th>Categoria</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "      <td>22</td>\n",
       "      <td>ARGE</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "      <td>17</td>\n",
       "      <td>COLO</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "      <td>93</td>\n",
       "      <td>ESPA</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "      <td>111</td>\n",
       "      <td>MEXI</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>916445</td>\n",
       "      <td>35</td>\n",
       "      <td>VENE</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie  Habit_x_km2 Codigo Categoria\n",
       "1  Argentina   45167000     1964375           22   ARGE         C\n",
       "3   Colombia   48922000     2780400           17   COLO         A\n",
       "2     Espana   47099000      505944           93   ESPA         B\n",
       "0     Mexico  127212000     1142748          111   MEXI         B\n",
       "4  Venezuela   32423000      916445           35   VENE         C"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def categoria(fila):\n",
    "    pob = fila['Poblacion']\n",
    "    habit = fila['Habit_x_km2']\n",
    "    if pob > 46000000:\n",
    "        if habit <50:\n",
    "            return 'A'\n",
    "        else:\n",
    "            return 'B'\n",
    "        \n",
    "    return 'C'\n",
    "\n",
    "df['Categoria'] = df.apply(categoria, axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aplicar enviando algunas columnas como parámetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.893419Z",
     "start_time": "2019-11-01T15:44:14.867583Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Habit_x_km2</th>\n",
       "      <th>Codigo</th>\n",
       "      <th>Categoria</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "      <td>22</td>\n",
       "      <td>ARGE</td>\n",
       "      <td>C</td>\n",
       "      <td>azul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "      <td>17</td>\n",
       "      <td>COLO</td>\n",
       "      <td>A</td>\n",
       "      <td>rojo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "      <td>93</td>\n",
       "      <td>ESPA</td>\n",
       "      <td>B</td>\n",
       "      <td>verde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "      <td>111</td>\n",
       "      <td>MEXI</td>\n",
       "      <td>B</td>\n",
       "      <td>azul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>916445</td>\n",
       "      <td>35</td>\n",
       "      <td>VENE</td>\n",
       "      <td>C</td>\n",
       "      <td>azul</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie  Habit_x_km2 Codigo Categoria  color\n",
       "1  Argentina   45167000     1964375           22   ARGE         C   azul\n",
       "3   Colombia   48922000     2780400           17   COLO         A   rojo\n",
       "2     Espana   47099000      505944           93   ESPA         B  verde\n",
       "0     Mexico  127212000     1142748          111   MEXI         B   azul\n",
       "4  Venezuela   32423000      916445           35   VENE         C   azul"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def asigna_color(codigo, categoria):\n",
    "    if categoria=='A':\n",
    "        return 'rojo'\n",
    "    if codigo == 'ESPA':\n",
    "        return 'verde'\n",
    "    return 'azul'\n",
    "\n",
    "df['color'] = df.apply(lambda x: asigna_color(x['Codigo'],x['Categoria']), axis=1);\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mapeo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.912983Z",
     "start_time": "2019-11-01T15:44:14.896382Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Habit_x_km2</th>\n",
       "      <th>Codigo</th>\n",
       "      <th>Categoria</th>\n",
       "      <th>color</th>\n",
       "      <th>mapeo_color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "      <td>22</td>\n",
       "      <td>ARGE</td>\n",
       "      <td>C</td>\n",
       "      <td>azul</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "      <td>17</td>\n",
       "      <td>COLO</td>\n",
       "      <td>A</td>\n",
       "      <td>rojo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "      <td>93</td>\n",
       "      <td>ESPA</td>\n",
       "      <td>B</td>\n",
       "      <td>verde</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "      <td>111</td>\n",
       "      <td>MEXI</td>\n",
       "      <td>B</td>\n",
       "      <td>azul</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>916445</td>\n",
       "      <td>35</td>\n",
       "      <td>VENE</td>\n",
       "      <td>C</td>\n",
       "      <td>azul</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais  Poblacion  Superficie  Habit_x_km2 Codigo Categoria  color  \\\n",
       "1  Argentina   45167000     1964375           22   ARGE         C   azul   \n",
       "3   Colombia   48922000     2780400           17   COLO         A   rojo   \n",
       "2     Espana   47099000      505944           93   ESPA         B  verde   \n",
       "0     Mexico  127212000     1142748          111   MEXI         B   azul   \n",
       "4  Venezuela   32423000      916445           35   VENE         C   azul   \n",
       "\n",
       "   mapeo_color  \n",
       "1            0  \n",
       "3            1  \n",
       "2            2  \n",
       "0            0  \n",
       "4            0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mapeo_color'] = df['color'].map( {'azul': 0, 'rojo': 1, 'verde': 2} ).astype(int)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reordenamos columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.934899Z",
     "start_time": "2019-11-01T15:44:14.916339Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Codigo</th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Categoria</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Habit_x_km2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ARGE</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>C</td>\n",
       "      <td>1964375</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>COLO</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>A</td>\n",
       "      <td>2780400</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ESPA</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>B</td>\n",
       "      <td>505944</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>MEXI</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>B</td>\n",
       "      <td>1142748</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>VENE</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>C</td>\n",
       "      <td>916445</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Codigo       Pais  Poblacion Categoria  Superficie  Habit_x_km2\n",
       "1   ARGE  Argentina   45167000         C     1964375           22\n",
       "3   COLO   Colombia   48922000         A     2780400           17\n",
       "2   ESPA     Espana   47099000         B      505944           93\n",
       "0   MEXI     Mexico  127212000         B     1142748          111\n",
       "4   VENE  Venezuela   32423000         C      916445           35"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[ ['Codigo','Pais','Poblacion','Categoria','Superficie','Habit_x_km2'] ]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Join entre tablas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concat usando indice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.952745Z",
     "start_time": "2019-11-01T15:44:14.937961Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comida</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>MEXI</td>\n",
       "      <td>Burritos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ARGE</td>\n",
       "      <td>Milanesa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ESPA</td>\n",
       "      <td>Tortilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>COLO</td>\n",
       "      <td>Sancocho</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VENE</td>\n",
       "      <td>Arepas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Comida\n",
       "MEXI  Burritos\n",
       "ARGE  Milanesa\n",
       "ESPA  Tortilla\n",
       "COLO  Sancocho\n",
       "VENE    Arepas"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creamos un DF nuevo, le asignamos el Codigo como identificador único\n",
    "df_comida = pd.DataFrame(data={\n",
    "                        \"Comida\":['Burritos', 'Milanesa', 'Tortilla', 'Sancocho','Arepas']},\n",
    "                        index = ['MEXI','ARGE','ESPA','COLO','VENE'])\n",
    "df_comida"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.973063Z",
     "start_time": "2019-11-01T15:44:14.955356Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Categoria</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Habit_x_km2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Codigo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ARGE</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>C</td>\n",
       "      <td>1964375</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>COLO</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>A</td>\n",
       "      <td>2780400</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ESPA</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>B</td>\n",
       "      <td>505944</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MEXI</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>B</td>\n",
       "      <td>1142748</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VENE</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>C</td>\n",
       "      <td>916445</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pais  Poblacion Categoria  Superficie  Habit_x_km2\n",
       "Codigo                                                         \n",
       "ARGE    Argentina   45167000         C     1964375           22\n",
       "COLO     Colombia   48922000         A     2780400           17\n",
       "ESPA       Espana   47099000         B      505944           93\n",
       "MEXI       Mexico  127212000         B     1142748          111\n",
       "VENE    Venezuela   32423000         C      916445           35"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#asignamos indice en nuestro DF inicial\n",
    "df_index = df.set_index('Codigo')\n",
    "df_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:14.995530Z",
     "start_time": "2019-11-01T15:44:14.979854Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Categoria</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Habit_x_km2</th>\n",
       "      <th>Comida</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ARGE</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>C</td>\n",
       "      <td>1964375</td>\n",
       "      <td>22</td>\n",
       "      <td>Milanesa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>COLO</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>A</td>\n",
       "      <td>2780400</td>\n",
       "      <td>17</td>\n",
       "      <td>Sancocho</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ESPA</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>B</td>\n",
       "      <td>505944</td>\n",
       "      <td>93</td>\n",
       "      <td>Tortilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MEXI</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>B</td>\n",
       "      <td>1142748</td>\n",
       "      <td>111</td>\n",
       "      <td>Burritos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VENE</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>C</td>\n",
       "      <td>916445</td>\n",
       "      <td>35</td>\n",
       "      <td>Arepas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pais  Poblacion Categoria  Superficie  Habit_x_km2    Comida\n",
       "ARGE  Argentina   45167000         C     1964375           22  Milanesa\n",
       "COLO   Colombia   48922000         A     2780400           17  Sancocho\n",
       "ESPA     Espana   47099000         B      505944           93  Tortilla\n",
       "MEXI     Mexico  127212000         B     1142748          111  Burritos\n",
       "VENE  Venezuela   32423000         C      916445           35    Arepas"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hacemos el join por indice\n",
    "result1 = pd.concat([df_index, df_comida], axis=1,sort=True)\n",
    "result1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Left join por columna clave (merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:15.010248Z",
     "start_time": "2019-11-01T15:44:14.998373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Categoria</th>\n",
       "      <th>Factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>12.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>103.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Categoria   Factor\n",
       "0         A   12.500\n",
       "1         B  103.000\n",
       "2         C    0.001"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imaginemos que tenemos un DF nuevo, le asignamos el Codigo como identificador único\n",
    "df_factor = pd.DataFrame(data={\"Categoria\" : [\"A\",\"B\",\"C\"],\n",
    "                            \"Factor\":[12.5,103,0.001]}\n",
    "                        )\n",
    "df_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:15.041154Z",
     "start_time": "2019-11-01T15:44:15.012815Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Codigo</th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Categoria</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Habit_x_km2</th>\n",
       "      <th>Factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ARGE</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>C</td>\n",
       "      <td>1964375</td>\n",
       "      <td>22</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>COLO</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>A</td>\n",
       "      <td>2780400</td>\n",
       "      <td>17</td>\n",
       "      <td>12.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ESPA</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>B</td>\n",
       "      <td>505944</td>\n",
       "      <td>93</td>\n",
       "      <td>103.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>MEXI</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>B</td>\n",
       "      <td>1142748</td>\n",
       "      <td>111</td>\n",
       "      <td>103.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>VENE</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>C</td>\n",
       "      <td>916445</td>\n",
       "      <td>35</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Codigo       Pais  Poblacion Categoria  Superficie  Habit_x_km2   Factor\n",
       "0   ARGE  Argentina   45167000         C     1964375           22    0.001\n",
       "1   COLO   Colombia   48922000         A     2780400           17   12.500\n",
       "2   ESPA     Espana   47099000         B      505944           93  103.000\n",
       "3   MEXI     Mexico  127212000         B     1142748          111  103.000\n",
       "4   VENE  Venezuela   32423000         C      916445           35    0.001"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2 = pd.merge(df, df_factor, how='left', on=['Categoria'])\n",
    "result2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adicionar multiples filas desde otra Tabla con Append"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:44:15.055827Z",
     "start_time": "2019-11-01T15:44:15.044875Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Brasil</td>\n",
       "      <td>210688000</td>\n",
       "      <td>8515770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Chile</td>\n",
       "      <td>19241000</td>\n",
       "      <td>56102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pais  Poblacion  Superficie\n",
       "0  Brasil  210688000     8515770\n",
       "1   Chile   19241000       56102"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#supongamos que tenemos otra tabla:\n",
    "df_otros = pd.DataFrame(data={\"Pais\":['Brasil','Chile'],\n",
    "                        \"Poblacion\":[210688000, 19241000],\n",
    "                        \"Superficie\":[8515770,56102]})\n",
    "df_otros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:46:06.559771Z",
     "start_time": "2019-11-01T15:46:06.537879Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Categoria</th>\n",
       "      <th>Codigo</th>\n",
       "      <th>Habit_x_km2</th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>ARGE</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>1964375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>COLO</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>ESPA</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>505944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
       "      <td>MEXI</td>\n",
       "      <td>111.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>1142748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>C</td>\n",
       "      <td>VENE</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>916445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brasil</td>\n",
       "      <td>210688000</td>\n",
       "      <td>8515770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chile</td>\n",
       "      <td>19241000</td>\n",
       "      <td>56102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Categoria Codigo  Habit_x_km2       Pais  Poblacion  Superficie\n",
       "0         C   ARGE         22.0  Argentina   45167000     1964375\n",
       "1         A   COLO         17.0   Colombia   48922000     2780400\n",
       "2         B   ESPA         93.0     Espana   47099000      505944\n",
       "3         B   MEXI        111.0     Mexico  127212000     1142748\n",
       "4         C   VENE         35.0  Venezuela   32423000      916445\n",
       "5       NaN    NaN          NaN     Brasil  210688000     8515770\n",
       "6       NaN    NaN          NaN      Chile   19241000       56102"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Queremos agregar estas filas al final\n",
    "df.append(df_otros, ignore_index=True, sort=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agrupar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:47:36.567983Z",
     "start_time": "2019-11-01T15:47:36.557822Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Categoria\n",
       "A    1\n",
       "B    2\n",
       "C    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# agrupo por categoria y sumo cuantos hay de cada una\n",
    "grupo2 = df.groupby(['Categoria']).size()\n",
    "grupo2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:47:26.846898Z",
     "start_time": "2019-11-01T15:47:26.831749Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Habit_x_km2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Categoria</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>48922000</td>\n",
       "      <td>2780400</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B</td>\n",
       "      <td>174311000</td>\n",
       "      <td>1648692</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>C</td>\n",
       "      <td>77590000</td>\n",
       "      <td>2880820</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Poblacion  Superficie  Habit_x_km2\n",
       "Categoria                                    \n",
       "A           48922000     2780400           17\n",
       "B          174311000     1648692          204\n",
       "C           77590000     2880820           57"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# agrupo por cateroría y sumo\n",
    "grupo1 = df.groupby(['Categoria']).sum()\n",
    "grupo1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agrupamos por 2 variables y sumarizamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:57:48.494522Z",
     "start_time": "2019-11-01T15:57:48.471806Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Factor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Categoria</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>12.500</td>\n",
       "      <td>1</td>\n",
       "      <td>12.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B</td>\n",
       "      <td>103.000</td>\n",
       "      <td>2</td>\n",
       "      <td>206.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>C</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Factor               \n",
       "              mean count      sum\n",
       "Categoria                        \n",
       "A           12.500     1   12.500\n",
       "B          103.000     2  206.000\n",
       "C            0.001     2    0.002"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabla = result2[['Categoria', 'Factor']].groupby(['Categoria'], as_index=False).agg(['mean', 'count', 'sum'])\n",
    "tabla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivotar una Tabla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T16:01:28.469318Z",
     "start_time": "2019-11-01T16:01:28.440453Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pais</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Colombia</th>\n",
       "      <th>Espana</th>\n",
       "      <th>Mexico</th>\n",
       "      <th>Venezuela</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Categoria</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>C</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pais       Argentina  Colombia  Espana  Mexico  Venezuela\n",
       "Categoria                                                \n",
       "A              0.000      12.5     0.0     0.0      0.000\n",
       "B              0.000       0.0   103.0   103.0      0.000\n",
       "C              0.001       0.0     0.0     0.0      0.001"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabla_t = pd.pivot_table(result2, index='Categoria', columns='Pais', values='Factor').fillna(0)\n",
    "tabla_t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transponer una tabla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T16:08:34.699663Z",
     "start_time": "2019-11-01T16:08:34.685897Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>2</th>\n",
       "      <th>0</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Codigo</td>\n",
       "      <td>ARGE</td>\n",
       "      <td>COLO</td>\n",
       "      <td>ESPA</td>\n",
       "      <td>MEXI</td>\n",
       "      <td>VENE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pais</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Espana</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Venezuela</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Poblacion</td>\n",
       "      <td>45167000</td>\n",
       "      <td>48922000</td>\n",
       "      <td>47099000</td>\n",
       "      <td>127212000</td>\n",
       "      <td>32423000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Categoria</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Superficie</td>\n",
       "      <td>1964375</td>\n",
       "      <td>2780400</td>\n",
       "      <td>505944</td>\n",
       "      <td>1142748</td>\n",
       "      <td>916445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Habit_x_km2</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>93</td>\n",
       "      <td>111</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1         3         2          0          4\n",
       "Codigo            ARGE      COLO      ESPA       MEXI       VENE\n",
       "Pais         Argentina  Colombia    Espana     Mexico  Venezuela\n",
       "Poblacion     45167000  48922000  47099000  127212000   32423000\n",
       "Categoria            C         A         B          B          C\n",
       "Superficie     1964375   2780400    505944    1142748     916445\n",
       "Habit_x_km2         22        17        93        111         35"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:35.805982Z",
     "start_time": "2019-11-01T15:48:35.411642Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df[['Poblacion','Superficie']].plot.hist(bins=5,alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:36.336928Z",
     "start_time": "2019-11-01T15:48:36.045568Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index('Pais')['Poblacion'].plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:36.884370Z",
     "start_time": "2019-11-01T15:48:36.687776Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index('Pais')['Habit_x_km2'].plot(kind='area');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:37.676445Z",
     "start_time": "2019-11-01T15:48:37.280687Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index('Pais').plot.barh(stacked=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:38.052042Z",
     "start_time": "2019-11-01T15:48:37.912306Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index('Pais')['Superficie'].plot.pie();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:38.810494Z",
     "start_time": "2019-11-01T15:48:38.586759Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SpGetWLaEy845iSMWD/HCJYs4YvEQl51zEiuWLdlvbTi4QJK0l7PWruK0E45mdPsuVi9ful9DBwweSdIkVixbst8DZ5yH2iRJnTJ4JEmdMngkSZ0yeCRJnRpY8CT5eJJHk3ylr3ZUkpuT3Neel7d6klyRZEuSzUlO7ltnfVv+viTr++qvTHJ3W+eKJJlrG5Kk7gxyj+cTwOkTapcAt1TVGuCWNg1wBrCmPS4AroReiACXAq8CTgEuHQ+StswFfeudPpc2JEndGljwVNUXgMcmlNcBV7fXVwNn99WvqZ7bgSOTHAO8Hri5qh6rqu3AzcDpbd6LquqLVVXANRO2tS9tSJI61PU5npdW1cMA7fklrb4KeKBvudFWm64+Okl9Lm08R5ILkowkGdm6des+vUFJ0vQOlMEFmaRWc6jPpY3nFquuqqrhqhpeuXLlDJuVJO2LroPnkfHDW+350VYfBY7tW2418NAM9dWT1OfShiSpQ10Hz43A+Mi09cANffXz2sizU4En2mGym4DXJVneBhW8DripzduR5NQ2mu28CdvalzYkSR0a2LXaknwKeDVwdJJReqPTPghcl+R84GvAm9vinwfOBLYATwJvA6iqx5K8H7izLfe+qhofsPCT9EbOLQX+oD3Y1zb0/G3buXtgFxOUtPCkNyhMUxkeHq6RkZH57sYBa9C3yJV0cEqysaqGJ5t3oAwu0EGoi1vkSlp4DB7NWRe3yJW08Bg8mrMubpEraeExeDRnXdwiV9LC4x1I9bwM+ha5khYeg0fP2yBvkStp4fFQmySpUwaPJKlTBo8kqVMGjySpUwaPJKlTBo8kqVMGjySpUwaPJKlTBo8kqVMGjySpUwaPJKlTBo8kqVMGjySpUwaPJKlTBo8kqVMGjySpUwaPJKlTBo8kqVMGjySpUwaPJKlTBo8kqVMGjySpUwaPJKlTBo8kqVMGjySpUwaPJKlTBo8kqVMGjySpU/MSPEnuT3J3kk1JRlrtqCQ3J7mvPS9v9SS5IsmWJJuTnNy3nfVt+fuSrO+rv7Jtf0tbN9O1IUmzsW3nbr78wONs27l7vrtyUJvPPZ4frKq1VTXcpi8BbqmqNcAtbRrgDGBNe1wAXAm9EAEuBV4FnAJc2hckV7Zlx9c7fYY2JGlaN2x6kNM+dCs//NE7OO1Dt3Ljpgfnu0sHrQPpUNs64Or2+mrg7L76NdVzO3BkkmOA1wM3V9VjVbUduBk4vc17UVV9saoKuGbCtiZrQ5KmtG3nbi7esJmn9oyxY/czPLVnjIs2bHbPZ47mK3gK+KMkG5Nc0GovraqHAdrzS1p9FfBA37qjrTZdfXSS+nRtSNKURrfvYvHQ3v+5XDw0xOj2XfPUo4Pbonlq97SqeijJS4Cbk/zVNMtmklrNoT5rLQwvAHjZy162L6tKWoBWL1/KnrGxvWp7xsZYvXzpPPXo4DYvezxV9VB7fhT4HL1zNI+0w2S050fb4qPAsX2rrwYemqG+epI607QxsX9XVdVwVQ2vXLlyrm9T0gKxYtkSLjvnJI5YPMQLlyziiMVDXHbOSaxYtmS+u3ZQ6nyPJ8kLgKGq2tFevw54H3AjsB74YHu+oa1yI/D2JNfSG0jwRFU9nOQm4Jf6BhS8Dnh3VT2WZEeSU4E7gPOAX+vb1mRtSNK0zlq7itNOOJrR7btYvXypofM8zMehtpcCn2sjnBcBv1tVf5jkTuC6JOcDXwPe3Jb/PHAmsAV4EngbQAuY9wN3tuXeV1WPtdc/CXwCWAr8QXtAL3Ama0OSZrRi2RIDZz9Ib+CXpjI8PFwjIyPz3Q1JOqgk2dj3c5m9HEjDqSVJhwCDR5LUKYNHktQpg0eS1CkHF8wgyVbg7wfczNHA1wfcxoHOz8DP4FB//7CwPoPvqKpJfwhp8BwAkoxMNfrjUOFn4GdwqL9/OHQ+Aw+1SZI6ZfBIkjpl8BwYrprvDhwA/Az8DA719w+HyGfgOR5JUqfc45EkdcrgkSR1yuDpWJJjk9yW5N4k9yR5R6sfleTmJPe15+UzbetgluSwJHcl+b9t+vgkd7T3/+kkh893HwcpyZFJrk/yV+278P2H4Hfgp9u/ga8k+VSSIxb69yDJx5M8muQrfbVJ/+7puSLJliSbk5w8fz3fvwye7j0DvKuqvgs4FbgwyYnAJcAtVbUGuKVNL2TvAO7tm/4QcHl7/9uB8+elV935H8AfVtU/Bb6X3mdxyHwHkqwC/jMwXFX/DDgMOJeF/z34BHD6hNpUf/czgDXtcQFwZUd9HDiDp2NV9XBVfam93kHvPzirgHXA1W2xq4Gz56eHg5dkNfBvgI+26QCvAa5viyz09/8i4AeAjwFU1dNV9TiH0HegWQQsTbII+DbgYRb496CqvgA8NqE81d99HXBN9dwOHDl+B+WDncEzj5IcB7yC3p1SX1pVD0MvnICXzF/PBu4jwEXA+E3sVwCPV9UzbXqUXhgvVC8HtgL/qx1u/Gi7G+8h8x2oqgeBX6F3Q8aHgSeAjRxa34NxU/3dVwEP9C23YD4Pg2eeJFkGbADeWVXfmO/+dCXJG4BHq2pjf3mSRRfyOP9FwMnAlVX1CuAfWMCH1SbTzmOsA44Hvh14Ab1DSxMt5O/BTBbsvwuDZx4kWUwvdD5ZVZ9t5UfGd6Pb86Pz1b8BOw04K8n9wLX0Dq18hN5hhPFbsa8GHpqf7nViFBitqjva9PX0guhQ+Q4A/Cvgq1W1tar2AJ8F/jmH1vdg3FR/91Hg2L7lFsznYfB0rJ3P+Bhwb1V9uG/WjcD69no9cEPXfetCVb27qlZX1XH0TibfWlX/EbgNeFNbbMG+f4Cq+n/AA0n+SSu9FvhLDpHvQPM14NQk39b+TYx/BofM96DPVH/3G4Hz2ui2U4Enxg/JHey8ckHHkvwL4E+Bu/nWOY6fo3ee5zrgZfT+Ub65qiaehFxQkrwa+NmqekOSl9PbAzoKuAv44araPZ/9G6Qka+kNrjgc+DvgbfT+R/CQ+Q4k+QXgLfRGet4F/Ci9cxgL9nuQ5FPAq+nd/uAR4FLg95jk794C+dfpjYJ7EnhbVY3MR7/3N4NHktQpD7VJkjpl8EiSOmXwSJI6ZfBIkjpl8EiSOmXwSJI6ZfBIz0OSnROm35rk12dY571JfnaS+rcnub69XpvkzP3Yz08kedPMS0667tokX2y3MNic5C37q186NBk80gGiqh6qqvFwWAvst+B5np4Ezquq76b3Y8aPJDlynvukg5jBIw1Ikn/bbmp2V5I/TvLSvtnfm+TWdvOvH2vLH9duinY48D7gLUk2TbWH0W4S9p72+vVJvpBkxn/TSd7f9oCGktyf5JfaHs1IkpOT3JTkb5P8BEBV/U1V3ddeP0TvWmIrn9eHo0PaopkXkTSNpUk29U0fRe8aWwB/BpxaVZXkR+ndCuJdbd5J9G4E+ALgriS/P76Bqnq6BcpwVb19mrYvAe5M8qfAFcCZVTU2zfIkuQx4Mb3Lr1Tvqiw8UFXfn+RyejcqOw04ArgH+J8T1j+F3mV+/na6dqTpGDzS87OrqtaOTyR5KzDcJlcDn25XHD4c+GrfejdU1S5gV5LbgFOA/gCbUVU92faWvgD8dFXNFAY/D9xRVRdMqI8H5d3AsnaDwh1JnkpyZLtJ3fiVk38HWD9TwEnT8VCbNDi/Bvx6VX0P8OP09iLGTbxI4lwvmvg9wDZ697SZyZ3AK5McNaE+fhHOsb7X49OL4Nm7pv4+8N/a3TClOTN4pMF5MfBge71+wrx1SY5IsoLe1YrvnDB/B/DC6Tae5DvoHbp7BXBGklfN0J8/BD4I/H6Sabc9oZ3Dgc/Ruw3zZ2a7njQVg0canPcCn2nnYL4+Yd5f0NuDuB14fztp3+824MSpBhf03dfpZ9u65wMfTXLExGX7teD4beDGJEtn+T7+PfADwFtbfza12zpIc+JtESRJnXKPR5LUKUe1SQe4JG8D3jGh/OdVdeEky/5X4M0Typ+pqg8Mqn/SvvJQmySpUx5qkyR1yuCRJHXK4JEkdcrgkSR16v8DnCyb5R0vrRUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='Habit_x_km2', y='Superficie');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basicos!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:42.272449Z",
     "start_time": "2019-11-01T15:48:42.269550Z"
    }
   },
   "outputs": [],
   "source": [
    "# Leer un archivo csv\n",
    "#df_csv = pd.read_csv(\"nombre_archivo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:42.479909Z",
     "start_time": "2019-11-01T15:48:42.471503Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5 entries, 1 to 4\n",
      "Data columns (total 6 columns):\n",
      "Codigo         5 non-null object\n",
      "Pais           5 non-null object\n",
      "Poblacion      5 non-null int64\n",
      "Categoria      5 non-null object\n",
      "Superficie     5 non-null int64\n",
      "Habit_x_km2    5 non-null int64\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 440.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# Informacion de columnas, tipos de datos y nulos\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:42.660761Z",
     "start_time": "2019-11-01T15:48:42.627756Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Habit_x_km2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>6.016460e+07</td>\n",
       "      <td>1461982.400</td>\n",
       "      <td>55.60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>3.803612e+07</td>\n",
       "      <td>908866.653</td>\n",
       "      <td>43.33359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>3.242300e+07</td>\n",
       "      <td>505944.000</td>\n",
       "      <td>17.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>4.516700e+07</td>\n",
       "      <td>916445.000</td>\n",
       "      <td>22.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>4.709900e+07</td>\n",
       "      <td>1142748.000</td>\n",
       "      <td>35.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>4.892200e+07</td>\n",
       "      <td>1964375.000</td>\n",
       "      <td>93.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.272120e+08</td>\n",
       "      <td>2780400.000</td>\n",
       "      <td>111.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Poblacion   Superficie  Habit_x_km2\n",
       "count  5.000000e+00        5.000      5.00000\n",
       "mean   6.016460e+07  1461982.400     55.60000\n",
       "std    3.803612e+07   908866.653     43.33359\n",
       "min    3.242300e+07   505944.000     17.00000\n",
       "25%    4.516700e+07   916445.000     22.00000\n",
       "50%    4.709900e+07  1142748.000     35.00000\n",
       "75%    4.892200e+07  1964375.000     93.00000\n",
       "max    1.272120e+08  2780400.000    111.00000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# estadísticas sobre las columnas numéricas\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:42.784227Z",
     "start_time": "2019-11-01T15:48:42.772163Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Codigo</th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Categoria</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Habit_x_km2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>MEXI</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>B</td>\n",
       "      <td>1142748</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>VENE</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>C</td>\n",
       "      <td>916445</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Codigo       Pais  Poblacion Categoria  Superficie  Habit_x_km2\n",
       "0   MEXI     Mexico  127212000         B     1142748          111\n",
       "4   VENE  Venezuela   32423000         C      916445           35"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ver primeras n filas  (ó ultimas)\n",
    "df.head(2)\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:42.927525Z",
     "start_time": "2019-11-01T15:48:42.921997Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 6)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dimensiones del dataframe (filas, columnas)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:43.063888Z",
     "start_time": "2019-11-01T15:48:43.058500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cantidad de filas\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:45.118320Z",
     "start_time": "2019-11-01T15:48:45.111800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Argentina\n",
      "Colombia\n",
      "Espana\n",
      "Mexico\n",
      "Venezuela\n"
     ]
    }
   ],
   "source": [
    "#Iterar un dataframe\n",
    "for index, row in df.iterrows():    \n",
    "    print(row['Pais'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T16:14:18.686615Z",
     "start_time": "2019-11-01T16:14:18.676083Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.6\n",
      "278\n",
      "5\n",
      "17\n",
      "111\n"
     ]
    }
   ],
   "source": [
    "# Obtener sumatoria, media y cantidad de una columna\n",
    "print(df['Habit_x_km2'].mean())\n",
    "print(df['Habit_x_km2'].sum())\n",
    "print(df['Habit_x_km2'].count())\n",
    "print(df['Habit_x_km2'].min())\n",
    "print(df['Habit_x_km2'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:48:45.404594Z",
     "start_time": "2019-11-01T15:48:45.396695Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Codigo         0\n",
       "Pais           0\n",
       "Poblacion      0\n",
       "Categoria      0\n",
       "Superficie     0\n",
       "Habit_x_km2    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revisar si tenemos nulos en la tabla\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:53:42.527742Z",
     "start_time": "2019-11-01T15:53:42.520437Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1964375\n",
       "3    2780400\n",
       "2     505944\n",
       "0    1142748\n",
       "4     916445\n",
       "Name: Superficie, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#acceso a una columna\n",
    "df.Superficie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:54:15.168930Z",
     "start_time": "2019-11-01T15:54:15.162817Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['C', 'A', 'B'], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unicos\n",
    "df.Categoria.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T15:54:38.800040Z",
     "start_time": "2019-11-01T15:54:38.794408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cantidad de unicos\n",
    "len(df.Categoria.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T16:21:27.013185Z",
     "start_time": "2019-11-01T16:21:27.005734Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    2\n",
       "C    2\n",
       "A    1\n",
       "Name: Categoria, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# contabilizar por una columna\n",
    "pd.value_counts(df['Categoria'], sort = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T16:24:48.876826Z",
     "start_time": "2019-11-01T16:24:48.869436Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Codigo              VENE\n",
       "Pais           Venezuela\n",
       "Poblacion       32423000\n",
       "Categoria              C\n",
       "Superficie        916445\n",
       "Habit_x_km2           35\n",
       "Name: 4, dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtener ultima fila\n",
    "df.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T16:25:29.673946Z",
     "start_time": "2019-11-01T16:25:29.667260Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    ARGE\n",
       "3    COLO\n",
       "2    ESPA\n",
       "0    MEXI\n",
       "4    VENE\n",
       "Name: Codigo, dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# obtener primera columna\n",
    "df.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T16:26:27.040151Z",
     "start_time": "2019-11-01T16:26:27.032513Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Codigo             ESPA\n",
       "Pais             Espana\n",
       "Poblacion      47099000\n",
       "Categoria             B\n",
       "Superficie       505944\n",
       "Habit_x_km2          93\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Busco por indice\n",
    "df.loc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T16:30:21.541499Z",
     "start_time": "2019-11-01T16:30:21.535748Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Codigo', 'Pais', 'Poblacion', 'Categoria', 'Superficie',\n",
       "       'Habit_x_km2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T16:29:00.459256Z",
     "start_time": "2019-11-01T16:29:00.449058Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pais</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Espana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Venezuela</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pais\n",
       "2     Espana\n",
       "4  Venezuela"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# obtner una columna dentro de una condicion\n",
    "df.loc[ df['Superficie'] < 1000000 ,[\"Pais\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-01T16:31:45.314846Z",
     "start_time": "2019-11-01T16:31:45.299595Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Codigo</th>\n",
       "      <th>Pais</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>Categoria</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Habit_x_km2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ARGE</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45167000</td>\n",
       "      <td>C</td>\n",
       "      <td>1964375</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>COLO</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>48922000</td>\n",
       "      <td>A</td>\n",
       "      <td>2780400</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ESPA</td>\n",
       "      <td>Espana</td>\n",
       "      <td>47099000</td>\n",
       "      <td>D</td>\n",
       "      <td>505944</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>MEXI</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>127212000</td>\n",
       "      <td>B</td>\n",
       "      <td>1142748</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>VENE</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32423000</td>\n",
       "      <td>D</td>\n",
       "      <td>916445</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Codigo       Pais  Poblacion Categoria  Superficie  Habit_x_km2\n",
       "1   ARGE  Argentina   45167000         C     1964375           22\n",
       "3   COLO   Colombia   48922000         A     2780400           17\n",
       "2   ESPA     Espana   47099000         D      505944           93\n",
       "0   MEXI     Mexico  127212000         B     1142748          111\n",
       "4   VENE  Venezuela   32423000         D      916445           35"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modificar Valor con loc\n",
    "df.loc[ df['Superficie'] < 1000000 ,\"Categoria\"] = 'D'\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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